
"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__			:							Any      =      logging.get_logger(__name__)

lowerCamelCase__			:							Union[str, Any]      =      {
    """microsoft/cvt-13""": """https://huggingface.co/microsoft/cvt-13/resolve/main/config.json""",
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}




class 					lowercase__(					a_ ):
	'''simple docstring'''

	UpperCamelCase				=	'''cvt'''





	def __init__(    self     :Dict		,			lowerCamelCase_     :Optional[Any]=3		,			lowerCamelCase_     :Any=[7, 3, 3]		,			lowerCamelCase_     :Optional[int]=[4, 2, 2]		,			lowerCamelCase_     :int=[2, 1, 1]		,			lowerCamelCase_     :List[Any]=[64, 1_92, 3_84]		,			lowerCamelCase_     :Tuple=[1, 3, 6]		,			lowerCamelCase_     :Union[str, Any]=[1, 2, 10]		,			lowerCamelCase_     :Dict=[4.0, 4.0, 4.0]		,			lowerCamelCase_     :str=[0.0, 0.0, 0.0]		,			lowerCamelCase_     :Dict=[0.0, 0.0, 0.0]		,			lowerCamelCase_     :int=[0.0, 0.0, 0.1]		,			lowerCamelCase_     :Union[str, Any]=[True, True, True]		,			lowerCamelCase_     :List[Any]=[False, False, True]		,			lowerCamelCase_     :Optional[Any]=["dw_bn", "dw_bn", "dw_bn"]		,			lowerCamelCase_     :Optional[Any]=[3, 3, 3]		,			lowerCamelCase_     :Dict=[1, 1, 1]		,			lowerCamelCase_     :int=[2, 2, 2]		,			lowerCamelCase_     :Any=[1, 1, 1]		,			lowerCamelCase_     :List[str]=[1, 1, 1]		,			lowerCamelCase_     :str=0.0_2		,			lowerCamelCase_     :Tuple=1E-12		,			**lowerCamelCase_     :Tuple		,			)				->      Tuple:







			'''simple docstring'''




			super().__init__(**_lowercase       )
			SCREAMING_SNAKE_CASE							:  str					=			num_channels
			SCREAMING_SNAKE_CASE							:  Optional[int]					=			patch_sizes
			SCREAMING_SNAKE_CASE							:  Dict					=			patch_stride
			SCREAMING_SNAKE_CASE							:  int					=			patch_padding
			SCREAMING_SNAKE_CASE							:  int					=			embed_dim
			SCREAMING_SNAKE_CASE							:  Tuple					=			num_heads
			SCREAMING_SNAKE_CASE							:  Optional[Any]					=			depth
			SCREAMING_SNAKE_CASE							:  str					=			mlp_ratio
			SCREAMING_SNAKE_CASE							:  Optional[int]					=			attention_drop_rate
			SCREAMING_SNAKE_CASE							:  int					=			drop_rate
			SCREAMING_SNAKE_CASE							:  List[Any]					=			drop_path_rate
			SCREAMING_SNAKE_CASE							:  List[Any]					=			qkv_bias
			SCREAMING_SNAKE_CASE							:  Dict					=			cls_token
			SCREAMING_SNAKE_CASE							:  Union[str, Any]					=			qkv_projection_method
			SCREAMING_SNAKE_CASE							:  List[Any]					=			kernel_qkv
			SCREAMING_SNAKE_CASE							:  Optional[Any]					=			padding_kv
			SCREAMING_SNAKE_CASE							:  Any					=			stride_kv
			SCREAMING_SNAKE_CASE							:  Dict					=			padding_q
			SCREAMING_SNAKE_CASE							:  Optional[int]					=			stride_q
			SCREAMING_SNAKE_CASE							:  Tuple					=			initializer_range
			SCREAMING_SNAKE_CASE							:  int					=			layer_norm_eps








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case				:      int   		=			{
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      int   		=			["""LlamaTokenizer"""]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      int   		=			["""LlamaTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      Union[str, Any]   		=			[
	    """LlamaForCausalLM""",
	    """LlamaModel""",
	    """LlamaPreTrainedModel""",
	    """LlamaForSequenceClassification""",
	]


if TYPE_CHECKING:
	from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama import LlamaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama_fast import LlamaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
	import sys

	__snake_case				:      Dict   		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


